code: def init_abst_summ_model(
      model_path,
      tokenizer_path,
      device,
      force_reload=False,
      min_length=20,
      max_length=50,
      batch_size=32,
      temperature=1.0,
      top_k=50,
      top_p=0.9,
      use_custom_api=True,
  ):
      global ABST_SUMM_MODELS
  
      model_name = "_".join(
          [
              os.path.basename(model_path),
              os.path.basename(tokenizer_path),
              str(device),
          ]
      )
      if model_name in ABST_SUMM_MODELS and not force_reload:
          ABST_SUMM_MODELS[model_name].min_length = min_length
          ABST_SUMM_MODELS[model_name].max_length = max_length
          ABST_SUMM_MODELS[model_name].temperature = temperature
          ABST_SUMM_MODELS[model_name].top_k = top_k
          ABST_SUMM_MODELS[model_name].top_p = top_p
          ABST_SUMM_MODELS[model_name].batch_size = batch_size
          return ABST_SUMM_MODELS[model_name]
  
      if use_custom_api:
          num_beam = 3
          no_repeat_ngram_size = 3
  
          if "t5" in model_path:
              model = nml.T5(
                  model_path,
                  device=device,
                  min_length=min_length,
                  max_length=max_length,
                  num_beam=num_beam,
                  no_repeat_ngram_size=no_repeat_ngram_size,
              )
          elif "bart-large-cnn" in model_path:
              model = nml.Bart(
                  model_path,
                  device=device,
                  min_length=min_length,
                  max_length=max_length,
                  num_beam=num_beam,
                  no_repeat_ngram_size=no_repeat_ngram_size,
              )
          else:
              raise ValueError(
                  "Model name value is unexpected. Only support `T5` and `bart-large-cnn` model."
              )
      else:
          model = nml.XSumTransformers(
              model_name=model_path,
              tokenizer_name=tokenizer_path,
              min_length=min_length,
              max_length=max_length,
              temperature=temperature,
              top_k=top_k,
              top_p=top_p,
              batch_size=batch_size,
              device=device,
          )
  
      ABST_SUMM_MODELS[model_name] = model
      return model
  
file_path: nlpaug/nlpaug/augmenter/sentence/abst_summ.py
imports: import os
  from nlpaug.augmenter.sentence import SentenceAugmenter
  import nlpaug.model.lang_models
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 1 for calling a global function with example usage: init_abst_summ_model(model_path, tokenizer_path, device, force_reload, min_length, max_length, batch_size, temperature, top_k, top_p, use_custom_api) and returns: model, ABST_SUMM_MODELS